The conditions of channels are important to the performance of wireless networks. Wireless signals in a non-line-of-sight (NLOS) channel often suffer greater path loss, and therefore are less reliable for communication than a line-of-sight (LOS) channel of equal distance. If a wireless network is capable of detecting whether a path between two nodes is one of line-of-sight (LOS), partially blocked direct path NLOS (NLOS-DP), or no direct path NLOS (NLOS-NDP), then the network can route data to a different path to improve communication reliability.
Impact of Channel Condition in ToA Based Ranging
The distance between two nodes in wireless networks can be estimated using the received signal strength (RSS), or the time-of-arrival (ToA). ToA-based ranging is based on measuring a time t form when the signal is transmitted to when the signal is received. The distance is estimated as d=t*c, where c is the travel speed of the signal in the medium (for example, the electromagnetic wave travels in free space at ˜3×108 meters per second.
FIG. 1A shows two transceivers A 101 and B 102 separated by distance d 103.
FIG. 1B shows that transceiver A transmits a wireless signal 111 at time instance t0, and the signal is received at transceiver B 102, after a time τ 123, at time t1=t0+τ. In ToA based ranging, time τ is the “flight” time used to estimate the distance d 103. The travel distance of the signal is estimated as d=τ*c.
A wireless channel can include many paths as shown in FIG. 2. The direct path 210 between transmitter and the receiver is referred to as the Line-of-Sight (LOS) path. Indirect paths that are reflective paths are referred to as the Non-Line-of-Sight (NLOS) paths.
The total travel distance of NLOS paths is greater than the LOS path. For instance, lengths 220 of two NLOS paths are d1+d2>d and d3+d4>d.
Generally the ToA is estimated based on the earliest arrival of the signal and the distance is{circumflex over (d)}ij=dij+zij+εij,where dij is the LOS distance, zij is the NLOS bias with the value of zero in LOS channels and positive in NLOS channels, and εij is a measure error, with a zero-mean Gaussian distribution.
FIG. 3A shows the power-delay-profile (PDP) of a LOS channel. The direct path is the strongest component, and appears at the time 301 when the direct path is expected. The ToA estimation therefore contains very small error.
FIG. 3B shows the PDP of a NLOS channel in which the direct path is attenuated, but detectable (NLOS-DP channel). The direct path is not the strongest component and there are some energy appears at the time when the direct path is expected. As a results, the error of the time 302 of arrival estimation is larger than in a LOS channel.
FIG. 3C shows the PDP of a NLOS channel in which the direct path is attenuated and completed not detectable (NLOS-NDP channel). The direct path is not the strongest component and cannot be detected at the time when the direct path is expected. The earliest detectable signal is at time 303, instead of time 301 or 302. As a result, the ToA estimation can have significantly larger error compared to two other channel conditions. The major portion of the error is contributed by the NLOS bias because zij>>τij.
Impact of Position Estimation in Wireless Networks
The localization of nodes in a wireless network can be performed using wireless signals. FIG. 4 shows an example network. Assuming a target node T 401, whose location is to be estimated, is wirelessly connected to M nodes A1, A2, . . . AM 402, with known locations. These nodes are referred to as anchor (A) nodes, and their locations are (x1, y1), (x2, y2), . . . (xM, yM). Also, assuming the distance estimation between the node T and the anchor node Ai, is available as {circumflex over (d)}i, then the location of the node T can be estimated as {circumflex over (θ)}=[{circumflex over (x)},ŷ], using a least square (LS) position estimator, which is
      θ    ^    =            arg              x        ,        y              ⁢                  ⁢          min      (                        [                                                    [                                  d                  -                                      F                    ⁡                                          (                                              θ                        ^                                            )                                                                      ]                            [                                                d                  ^                                -                                  F                  ⁡                                      (                                          θ                      ^                                        )                                                              ]                        T                    )                ,            where {circumflex over (d)}=[{circumflex over (d)}1 . . . {circumflex over (d)}M] is a range measurement vector, and F({circumflex over (θ)}) is the computed distance at the estimated location of the node T location, given as
      F    ⁡          (              θ        ^            )        =            [                                                                                                        (                                                                  x                        ^                                            -                                              x                        1                                                              )                                    2                                +                                                      (                                                                  y                        ^                                            -                                              y                        1                                                              )                                    2                                                                                          …                                                                                                                    (                                                                  x                        ^                                            -                                              x                        M                                                              )                                    2                                +                                                      (                                                                  y                        ^                                            -                                              y                        M                                                              )                                    2                                                                        ]        .  
The LS estimator treats each estimated distance, {circumflex over (d)}i equally. However, the distance {circumflex over (d)}1 can be inaccurate because it a reflected by an object 410. If the distance estimation is accurate, then the position solver returns the estimated position of node T, i.e., {circumflex over (θ)}=[xT,yT].
If errors in all of the distance estimations are equal or close, then the LS estimation using all available anchor nodes generally returns a more accurate estimation compared to an LS estimation using only a subset of the anchor nodes.
If one or more distance estimations contain error, which is significantly larger than others, the LS position estimator, or any other method that treats all distances equally, produces results with an increased error.
If a given distance measurement {circumflex over (d)}i has a large error, then a position estimation method, which discriminates the distance measurements during the position estimation, can be used to achieve position error performance improvement. One of such methods uses a weighted-least-square (WLS)
      θ    ^    =            arg              x        ,        y              ⁢          min      (                        [                                    [                                                d                  ^                                -                                  F                  ⁡                                      (                                          θ                      ^                                        )                                                              ]                        ⁢                                          W                [                                                      d                    ^                                    -                                      F                    ⁡                                          (                                              θ                        ^                                            )                                                                      ]                            T                                )                ,            where W=[W1 W2 . . . WM] is a weight vector, Wi is the weight assigned to the ith distance measurement. A larger weight is assigned to a distance measurement with a greater confidence score. Conversely, if a distance measurement has a large error and low confidence, a small weight is assigned.
For the WLS method to have good performance, the correct weight assignment is critical.
Prior Art Channel Classification
Several channel condition classification methods are known.
“Channel statistics” (such as RMS delay spread) can be used to identify NLOS channels. That method is computationally complex, and energy inefficient, because multiple range measurements are needed per channel to determine channel statistics.
“Frequency diversity” can also be used to identify the direct-path blockage. Based on channel measurements in a typical indoor environment, the variation of ToA estimation across frequency sub-bands has a positive correlation with the channel condition. That approach requires a frequency hopping capable radio frequency (RF) front end, and therefore the transceivers have higher cost, complexity, and power consumption. It is also difficult to isolate the frequency dependency of the antennas from the channel, which directly impacts the effectiveness of that approach.
“Running variance” is another method for channel condition identification. It computes the variance of subsequent range estimates, and compares the variances with a predetermined threshold to decide between LOS and NLOS. That method has high computation complexity, and is energy inefficient.
“Change of SNR” method detects sudden change of SNR to determine whether the channel is moving from LOS to NLOS, or vise versa.